Controlling exoskeleton through brain signals

In this April 17, 2015 photo, Yuichi Imahata walks using a robotic exoskeleton called ReWalk at Kanagawa Rehabilitation Center in Atgugi, west of Tokyo. Imahata, 31, has been using a wheelchair to get around for seven years after a serious spinal-cord injury suffered in an accidental fall while working for a transport company. He completely lost sensation in both his legs and was told he would never walk again.Photo/Shuji Kajiyama)LONDON : Scientists have developed a brain-computer control interface for a lower limb exoskeleton by decoding specific signals from within the user's brain.

Using an electroencephalogram (EEG) cap, the system allows users to move forward, turn left and right, sit and stand simply by staring at one of five flickering light emitting diodes (LEDs).

Each of the five LEDs flickers at a different frequency, and when the user focuses their attention on a specific LED this frequency is reflected within the EEG readout.

This signal is identified and used to control the exoskeleton.

The system has the potential to aid sick or disabled people, said the researchers from Korea University, Korea, and TU Berlin, Germany.

"People with amyotrophic lateral sclerosis (ALS), or high spinal cord injuries face difficulties communicating or using their limbs" said paper co-author Klaus Muller.

"Decoding what they intend from their brain signals could offer means to communicate and walk again," Muller said.

A key problem has been separating these precise brain signals from those associated with other brain activity, and the highly artificial signals generated by the exoskeleton.

The control system could serve as a technically simple and feasible add-on to other devices, with EEG caps and hardware now emerging on the consumer market.

The researchers are now working to reduce the 'visual fatigue' associated with longer-term users of such systems.

"We were driven to assist disabled people, and our study shows that this brain control interface can easily and intuitively control an exoskeleton system despite the highly challenging artefacts from the exoskeleton itself," Muller said.

The results published on Tuesday in the Journal of Neural Engineering.

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